Could prioritisation by emergency medicine dispatchers be improved by using computer-based decision support? A cohort of patients with chest pain. (1st October 2016)

• Record Type: Journal Article
• Authors: Gellerstedt, Martin; Rawshani, Nina; Herlitz, Johan; Bång, Angela; Gelang, Carita; Andersson, Jan-Otto; Larsson, Anna; Rawshani, Araz
• Is Part Of: International journal of cardiology. Volume 220(2016)
• Page Start: 734
• Page End: 738
• Publication Date: 2016-10-01
• Subjects: Prehospital -- Chest pain -- ACS -- Mortality -- Decision support model; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2016.06.281
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: To evaluate whether a computer-based decision support system could improve the allocation of patients with acute coronary syndrome (ACS) or a life-threatening condition (LTC). We hypothesised that a system of this kind would improve sensitivity without compromising specificity. Methods: A total of 2285 consecutive patients who dialed 112 due to chest pain were asked 10 specific questions and a prediction model was constructed based on the answers. We compared the sensitivity of the dispatchers' decisions with that of the model-based decision support model. Results: A total of 2048 patients answered all 10 questions. Among the 235 patients with ACS, 194 were allocated the highest prioritisation by dispatchers (sensitivity 82.6%) and 41 patients were given a lower prioritisation (17.4% false negatives). The allocation suggested by the model used the highest prioritisation in 212 of the patients with ACS (sensitivity of 90.2%), while 23 patients were underprioritised (9.8% false negatives). The results were similar when the two systems were compared with regard to LTC and 30-day mortality. This indicates that computer-based decision support could be used either for increasing sensitivity or for saving resources. Three questions proved to be most important in terms of predicting ACS/LTC, [1] the intensity of pain, [2] the localisation of pain and [3] a history of ACS. Conclusion: Among patients with acute chest pain, computer-based decision support with a model based on a few fundamental questions could improve sensitivity and reduce the number of cases with the highest prioritisation without endangering the patients.